Ethernet-based industrial communication standards are dominating the communication landscape in factories. With the transition towards more flexible and wireless solutions, there is a strong interest to enable existing wired applications, such as control-to-control (C2C) use cases, over a wireless network. In this work, we investigate an example C2C application and validate its performance when operated over 5G. For this purpose, we present measurement results taken from a 5G standalone (SA) deployment in an operational factory. Our results show that 5G can replace existing wired solution, but at the cost of a lowered C2C application efficiency due to longer network latency. We furthermore investigate the impact of cross-traffic to the C2C application and effects of traffic prioritization.

5G networks are expected to support numerous novel services and applications with versatile quality of service (QoS) requirements such as high data rates and low end-to-end (E2E) latency. It is widely agreed that E2E latency can be reduced by moving the computational capability closer to the network edge. The limited amount of computational resources of the edge nodes, however, poses the challenge of efficiently utilizing these resources while, at the same time, satisfying QoS requirements. In this work, we employ mixed-integer linear programming (MILP) techniques to formulate and solve a joint user association, service function chain (SFC) placement, where SFCs are composed of virtualized service functions (VSFs), and resource allocation problem in 5G networks composed of decentralized units (DUs), centralized units (CUs), and a core network (5GC). Specifically, we compare four approaches to solving the problem. The first two approaches minimize, respectively, the E2E latency experienced by users and the service provisioning cost. The other two instead aim at minimizing VSF migrations along with their impact on users’ quality of experience with the last one minimizing also the number of inter-CU handovers. We then propose a heuristic to address the scalability issue of the MILP-based solutions. Simulations results demonstrate the effectiveness of the proposed heuristic algorithm.

Dynamic Adaptive Streaming over HTTP (DASH) is a standard for delivering video in segments and adapting each segment’s bitrate (quality), to adjust to changing and limited network bandwidth. We study segment prefetching, informed by machine learning predictions of bitrates of client segment requests, implemented at the network edge. We formulate this client segment request prediction problem as a supervised learning problem of predicting the bitrate of a client’s next segment request, in order to prefetch it at the mobile edge, with the objective of jointly improving the video streaming experience for the users and network bandwidth utilization for the service provider. The results of extensive evaluations showed a segment request prediction accuracy of close to 90% and reduced video segment access delay with a cache hit ratio of 58%, and reduced transport network load by lowering the backhaul link utilization by 60.91%.

The ongoing roll-out of 5G networks paves the way for many fascinating applications such as virtual reality (VR), augmented reality (AR), and autonomous driving. Moreover, 5G enables billions of devices to transfer an unprecedented amount of data at the same time. This transformation calls for novel technologies like multi-access edge computing (MEC) to satisfy the stringent latency and bitrate requirements of the mentioned applications. The main challenge pertaining to MEC is that the edge MEC nodes are usually characterized by scarce computational resources compared to the core or cloud, arising the challenge of efficiently utilizing the edge resources while ensuring that the service requirements are satisfied. When considered with the users' mobility, this poses another challenge, which lies in minimization of the service interruption for the users whose service requests are represented as service function chains (SFCs) composed of virtualized network functions (VNFs) instantiated on the MEC nodes or on the cloud. In this paper, we study the problem of joint user association, SFC placement, and resource allocation, employing mixed-integer linear programming (MILP) techniques. The objective functions of this MILP-based problem formulation are to minimize (i) the service provisioning cost, (ii) the transport network utilization, and (iii) the service interruption. Moreover, a heuristic algorithm is proposed to tackle the scalability issue of the MILP-based algorithms. Finally, comprehensive experiments are performed to draw a comparison between these approaches.

Streaming high-quality video over dynamic radio networks is challenging. Dynamic adaptive streaming over HTTP (DASH) is a standard for delivering video in segments, and adapting its quality to adjust to a changing and limited network bandwidth. We present a machine learning-based predictive pre-fetching and caching approach for DASH video streaming, implemented at the multi-access edge computing server. We use ensemble methods for machine learning (ML) based segment request prediction and an integer linear programming (ILP) technique for pre-fetching decisions. Our approach reduces video segment access delay with a cache-hit ratio of 60% and alleviates transport network load by reducing the backhaul link utilization by 69%. We validate the ML model and the pre-fetching algorithm, and present the trade-offs involved in pre-fetching and caching for resource-constrained scenarios.

5G networks are characterized by massive device connectivity, supporting a wide range of novel applications with their diverse Quality of Service (QoS) requirements. This poses a challenge since 5G as one-fits-all technology has to simultaneously address all these requirements. Network slicing has been proposed to cope with this challenge, calling for efficient slicing and slice placement strategies in order to ensure that the slice requirements (e.g., latency, data rate) are met, while the network resources are utilized in the most optimal manner. In this paper, we compare different end-to-end (E2E) slice placement strategies by formulating and solving a Mixed Integer Linear Programming (MILP) slice placement problem and study their trade-offs. E2E slice requests are modelled as Service Functions Chains (SFC), in which each core network and radio access network component is represented as a Virtual Network Function (VNF). Based on the analysis of the results, we then propose a slice placement heuristic algorithm whose objective is to minimize the number of VNF migrations in the network and their impact onto the slices while, at the same time, optimizing the network utilization and making sure that the QoS requirements of the considered slice requests are satisfied. The results of the simulations demonstrate the efficiency of the proposed algorithm.

The 5th generation mobile network (5G) is expected to support numerous services with versatile quality of service (QoS) requirements such as high data rates and low end-to-end (E2E) latency. It is widely agreed that E2E latency can be significantly reduced by moving content/computing capability closer to the network edge. However, since the edge nodes (i.e., base stations) have limited computing capacity, mobile network operators shall make a decision on how to provision the computing resources to the services in order to make sure that the E2E latency requirement of the services are satisfied while the network resources (e.g., computing, radio, and transport network resources) are used in an efficient manner. In this work, we employ integer linear programming (ILP) techniques to formulate and solve a joint user association, service function chain (SFC) placement, and resource allocation problem where SFCs, composed of virtualized service functions (VSFs), represent user requested services that have certain E2E latency and data rate requirements. Specifically, we compare three variants of an ILP-based algorithm that aim to minimize E2E latency of requested services, service provisioning cost, and VSF migration frequency, respectively. We then propose a heuristic in order to address the scalability issue of the ILP-based solutions. Simulations results demonstrate the effectiveness of the proposed heuristic algorithm.

The increasing demand for live streaming and for remote sensing applications is bringing renewed interest on uplink performances in Wi-Fi networks. Radio diversity can improve the performance of such applications by opportunisti-cally receiving mobile users' traffic at multiple attachment points. However, radio diversity techniques can not be used in standard Wi-Fi networks due to backwards compatibility problems. In this paper we present Wi-Not, a novel SDN-based solution for exploiting radio diversity in software-defined WLANs. Wi-Not allows mobile terminals to be associated to multiple Wi-Fi APs in the uplink direction improving frame delivery probability in uplink-constrained applications. Wi-Not does not require changes to the mobile terminals and can be easily deployed with minimal changes to the network infrastructure. An experimental evaluation carried out over a real-world testbed shows that this approach can deliver an improvement of up to 80% in terms of UDP goodput and up to 60% of TCP throughput. We release the entire implementation including the controller and the data-path under a permissive license for academic use.

WiFi networks are known to be a cost-efficient traffic offloading solution for mobile networks. The Multi Access Packet Data Network Connectivity is a feature introduced in LTE Release 10 in order to allow users to be simultaneously connected to multiple radio access networks (RAN). Although this feature brings many advantages, such as the possibility to implement QoS-based traffic steering, it poses also many challenges, one of which is distributing traffic among the two radio access technologies. In this paper, we propose a traffic- aware user association algorithm for heterogeneous LTE/WiFi RANs. The proposed algorithm is formulated as an Integer Linear Programming (ILP) problem jointly optimizing user association and resource allocation. A heuristic is also proposed in order to address the scalability issues of the ILP-based algorithm. Numerical simulations are used in order to compare the proposed approaches. Finally, we implemented and tested the heuristic in small-scale testbed using the 5G-EmPOWER platform.

5G networks are expected to support various applications with diverse requirements in terms of latency, data rates, and traffic volume. Cloud-RAN(C-RAN) and densely deployed small cells are two of the tools at disposal of mobile network operators to cope with such challenges. In order to mitigate the fronthaul requirements imposed by the C-RAN architecture, several functional splits, each characterized by a different demarcation point between the centralized and the distributed units, have emerged. However, the selection of the appropriate centralization level (i.e., the functional split) still remains a challenging task, since a number of parameters have to be considered in order to make such a decision. In this paper, a virtual network embedding (VNE) algorithm is proposed to flexibly select the appropriate functional split for each small cell. The VNE is formulated as an integer linear programming (ILP) problem whose objective is to jointly minimize the inter-cell interference and the fronthaul bandwidth utilization by dynamically selecting the appropriate functional split. Specifically, dynamic and static ILP-based algorithms are proposed. Finally, dynamic and static VNE heuristics are proposed to address the scalability problem of the ILP-based algorithms in case of dense and ultra-dense mobile networks, respectively.

By leveraging the fully-centralized and virtualized cloud radio access network (C-RAN) architecture over densely deployed small cells, mobile network operators (MNOs) are expected to meet the ever-increasing coverage and capacity demands. Towards this end, finding the optimal numbers, and locations of centralized unit (CU) pools, and centralizing the baseband units of eNBs at the optimal CU pools plays a pivotal role in curtailing the required investments in order to transit from legacy decentralized RAN (D-RAN) to C-RAN. In this paper, we propose an approach for MNOs to adopt the C-RAN architecture with minimal investment by using the available infrastructure (e.g., site locations and transmission links). Specifically, we propose a decentralized unit - CU (DU-CU) mapping algorithm, which effectively selects the quantity and the locations of CU pools and assigns the CU of each DU to the appropriate CU pool. We then compare the traffic aggregation gains of C-RAN and traditional D-RAN. Lastly, in order to quantify the total cost of ownership savings that can be obtained by employing the legacy network infrastructure while migrating to C-RAN, we compare this scenario with the C-RAN migration scenario in which there is no available infrastructure. In both scenarios, the mapping algorithms are formulated as virtual network embedding problems using integer linear programming techniques. The results of the simulations, conducted using data traffic of 26 eNBs (209 cells) of an operational LTE-A mobile network, reveal that significant saving can be obtained by employing the available mobile network infrastructure while migrating to C-RAN.

5G networks are expected to support various applications with diverse requirements in terms of latency, data rates and traffic volume. Cloud-RAN and densely deployed small cells are two of the tools at disposal of Mobile Network Operators to cope with such challenges. In order to mitigate the fronthaul requirements imposed by the Cloud-RAN architecture, several functional splits, each characterized by a different demarcation point between the centralized and the distributed units, have emerged. However, the selection of the appropriate centralization level (i.e., the functional split) still remains a challenging task, since a number of parameters have to be considered in order to make such a decision. In this paper, a virtual network embedding (VNE) algorithm is proposed to flexibly select the appropriate functional split. The VNE is formulated as an Integer Linear Programming problem whose objective is to minimize the intercell interference and the fronthaul bandwidth utilization by dynamically selecting the appropriate functional split. Finally, a scalable VNE heuristic is also proposed.

Small-cells are rapidly emerging as the mobile operators' choice to provide additional capacity in current and future mobile networks. However, in order to fully deliver on their promises, small-cells need to address severe interference control and coordination challenges. By centralizing base-band processing in large high-volume computing infrastructures, Cloud-RAN can effectively enable advanced coordination features for dense small-cells deployments. Unfortunately, Cloud-RAN tight bandwidth and latency requirements have made optical fiber the most common solution for the links interconnecting remote radio heads (RRHs) with the base band units (BBUs), i.e. the fronthaul. Recent advances in microwave communications are making wireless fronthauls a viable option especially in dense urban environments where fiber fronthauls could be too rigid for accommodating highly dynamic traffic patterns. In this paper, we provide a novel formulation for the BBU Placement problem where BBU pools are placed at the edges of the network, possibly co-located with macro-cells, and a reconfigurable wireless fronthaul is used in order to provide RRHs with connectivity. To the best of our knowledge this is the first work to tackle the BBU placement problem over a reconfigurable substrate network with mmWave links. We also propose a BBU Placement heuristics, and we evaluate it using a numerical simulator.

Network function virtualization (NFV) sits firmly on the networking evolutionary path. By migrating network functions from dedicated devices to general purpose computing platforms, NFV can help reduce the cost to deploy and operate large IT infrastructures. In particular, NFV is expected to play a pivotal role in mobile networks where significant cost reductions can be obtained by dynamically deploying and scaling virtual network functions (VNFs) in the core network. However, in order to achieve its full potential, NFV needs to extend its reach also to the radio access segment. Here, mobile virtual network operators shall be allowed to request radio access VNFs with custom resource allocation solutions. Such a requirement raises several challenges in terms of performance isolation and resource provisioning. In this work, we formalize the wireless VNF placement problem in the radio access network as an integer linear programming problem and we propose a VNF placement heuristic, named wireless network embedding (WiNE), to solve the problem. Moreover, we present a proof-of-concept implementation of an NFV management and orchestration framework for enterprise WLANs. The proposed architecture builds on a programmable network fabric where pure forwarding nodes are mixed with radio and packet processing capable nodes.

Design of integrated RF circuits requires detailed insight in the behavior of the used components. Unintended coupling and perturbation effects need to be accounted for before production, but full simulation of these effects can be expensive or infeasible. In this paper, we present a method to build nonlinear phase macromodels of voltage-controlled oscillators. These models can be used to accurately predict the behavior of individual and mutually coupled oscillators under perturbation at a lower cost than full circuit simulations. The approach is illustrated by numerical experiments with realistic designs.